* feat: MCP HTTP transport with daemon lifecycle
Add streaming HTTP transport as an alternative to stdio for the MCP
server. A long-lived HTTP server avoids reloading 3 GGUF models (~2GB)
on every client connection, reducing warm query latency from ~16s (CLI)
to ~10s.
New CLI surface:
qmd mcp --http [--port N] # foreground, default port 3000
qmd mcp --http --daemon # background, PID in ~/.cache/qmd/mcp.pid
qmd mcp stop # stop daemon via PID file
qmd status # now shows MCP daemon liveness
Server implementation (mcp.ts):
- Extract createMcpServer(store) shared by stdio and HTTP transports
- HTTP transport uses WebStandardStreamableHTTPServerTransport with
JSON responses (stateless, no SSE)
- /health endpoint with uptime, /mcp for MCP protocol, 404 otherwise
- Request logging to stderr with timestamps, tool names, query args
Daemon lifecycle (qmd.ts):
- PID file + log file management with stale PID detection
- Absolute paths in Bun.spawn (process.execPath + import.meta.path)
so daemon works regardless of cwd
- mkdirSync for cache dir on fresh installs
- Removes top-level SIGTERM/SIGINT handlers before starting HTTP
server so async cleanup in mcp.ts actually runs
Move hybridQuery() and vectorSearchQuery() into store.ts as standalone
functions that take a Store as first argument. Both CLI and MCP now
call the identical pipeline, eliminating the class of bugs where one
copy drifts from the other.
Shared pipeline (store.ts):
- hybridQuery(): BM25 probe → expand → FTS+vec search → RRF →
chunk → rerank (chunks only) → position-aware blending → dedup
- vectorSearchQuery(): expand → vec search → dedup → sort
- SearchHooks interface for optional progress callbacks
- Constants: STRONG_SIGNAL_MIN_SCORE, STRONG_SIGNAL_MIN_GAP,
RERANK_CANDIDATE_LIMIT (40), addLineNumbers()
Bugs fixed by unification:
- MCP now gets strong-signal short-circuit (was CLI-only)
- Reranker candidate limit unified at 40 (MCP had 30)
- File dedup added to hybrid query (MCP was missing it)
- Collection filter pushed into searchVec DB query
- Filter-then-slice ordering fixed (MCP was slice-then-filter)
* feat: type-routed query expansion — lex→FTS, vec/hyde→vector
expandQuery() now returns typed ExpandedQuery[] instead of string[],
preserving the lex/vec/hyde type info from the LLM's GBNF-structured
output. hybridQuery() and vectorSearchQuery() route searches by type:
lex queries go to FTS only, vec/hyde go to vector only.
Previously, every expanded query ran through BOTH backends — keyword
variants wasted embedding forward passes, semantic paraphrases wasted
BM25 lookups. Type routing eliminates ~4 calls/query with zero quality
loss (cross-backend noise actually hurt RRF fusion).
Cache format changed from newline-separated text to JSON (preserves
types). Old cache entries gracefully re-expand on first access.
CLI expansion tree now shows query types:
├─ original query
├─ lex: keyword variant
├─ vec: semantic meaning
└─ hyde: hypothetical document...
Benchmark (5 queries, 1756-doc index, warm LLM, Apple Silicon):
Metric Old (untyped) New (typed) Delta
Avg backend calls 10.0 6.0 -40%
Total wall time 1278ms 549ms -57%
Avg saved/query — — 146ms
"authentication setup" 12 → 7 calls 511 → 112ms
"database migration strategy" 10 → 6 calls 182 → 106ms
"how to handle errors in API" 10 → 6 calls 216 → 121ms
"meeting notes from last week" 10 → 6 calls 228 → 110ms
"performance optimization" 8 → 5 calls 141 → 100ms
Savings come from skipped embed() calls (~30-80ms each). FTS is
synchronous SQLite (~0ms), so lex→FTS routing is free while
vec/hyde→vector-only avoids wasted embedding passes.
* fix: MCP query snippets now use reranker's best chunk, not full body
extractSnippet() was scanning the entire document body for keyword
matches to build the snippet. But hybridQuery() already identified
the most relevant chunk via cross-attention reranking — rescanning
the full body is redundant and can land on a less relevant section
if the query terms appear elsewhere in the document.
CLI was already using bestChunk (set during the refactor). MCP was
still using body — a pre-existing inconsistency, not a regression.
* feat: dynamic MCP instructions + tool annotations
The MCP server now generates instructions at startup from actual index
state and injects them into the initialize response. LLMs see collection
names, document counts, content descriptions, and search strategy
guidance in their system prompt — zero tool calls needed for orientation.
Previously, the only guidance was generic static tool descriptions and
a user-invocable "query" prompt that no LLM would discover on its own.
An LLM connecting to QMD had no idea what collections existed, what they
contained, or how to scope searches effectively.
* change default port to 8181
* fix: BM25 score normalization was inverted
The normalization formula `1 / (1 + |bm25|)` is a decreasing function of
match strength. FTS5 BM25 scores are negative where more negative = better
match (e.g., -10 is strong, -0.5 is weak). The formula mapped:
strong match (raw -10) → 1/(1+10) = 9% ← should be highest
weak match (raw -0.5) → 1/(1+0.5) = 67% ← should be lowest
Three downstream effects:
1. `--min-score 0.5` (or MCP minScore: 0.5) filtered OUT strong matches
and kept only weak ones. The MCP instructions recommend this threshold.
2. CLI `formatScore()` color bands never showed green for BM25 results
(best matches scored ~9%, green threshold is 70%).
3. The strong signal optimization in hybridQuery (skip ~2s LLM expansion
when BM25 already has a clear winner) was dead code — strong matches
scored ~0.09, never reaching the 0.85 threshold.
Fix: `|x| / (1 + |x|)` — same (0,1) range, monotonic, no per-query
normalization needed, but now correctly maps strong → high, weak → low.
The normalization was born broken (Math.max(0, x) clamped all
negative BM25 to 0 → every score = 1.0), then PR #76 changed to
Math.abs which made scores vary but inverted the direction. Neither
state was ever correct.
* fix: rerank cache key ignores chunk content
The rerank cache key was (query, file, model) but the actual text sent
to the reranker is a keyword-selected chunk that varies by query terms.
Two different queries hitting the same file can select different chunks,
but the second query gets a stale cached score from the first chunk.
Example:
Query "auth flow" → selects chunk about authentication → score 0.92
Query "auth tokens" → same file, selects chunk about tokens
→ cache HIT on (query, file, model) → returns 0.92 from wrong chunk
Fix: include full chunk text in cache key. getCacheKey() already
SHA-256 hashes its inputs, so this adds no key bloat — just
disambiguation. Old cache entries become natural misses (different key
shape) and re-warm on next query.
* rename MCP tools for clarity, rewrite descriptions for LLM tool selection
Rename MCP tools: vsearch → vector_search, query → deep_search.
LLMs see these names — self-documenting names reduce reliance on
descriptions for tool selection. CLI commands stay unchanged
(qmd vsearch, qmd query) — different namespace, users type those.
Rewrite all search tool descriptions to be action-oriented:
- search: "Search by keyword. Finds documents containing exact
words and phrases in the query."
- vector_search: "Search by meaning. Finds relevant documents even
when they use different words than the query — handles synonyms,
paraphrases, and related concepts."
- deep_search: "Deep search. Auto-expands the query into variations,
searches each by keyword and meaning, and reranks for top hits
across all results."
Rewrite instructions ladder — each tool says what it does, no
"start here" / "escalate as needed" strategy language.
Delete the "query" prompt (registerPrompt) — it restated what
descriptions + instructions already cover. No LLM proactively
calls prompts/get to learn how to use tools.
* supress HTTP server logs during tests
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